local ai models

A Bash-first Linux guide to running private, fast local AI: why go local (privacy, latency, cost, repeatability); how to choose formats (GGUF, safetensors) and runtimes (llama.cpp, Transformers, optional Ollama); steps to install deps, fetch and catalog models with manifests and checksums, run reproducibly, cap CPU/RAM (systemd, nice, cpulimit), host a service, benchmark, quantize, and apply real-world examples.

Guide to running AI models locally on Linux for privacy, predictable costs, and offline use. Explains choosing model families and quantizations, RAM/VRAM needs, and licenses; fast start with Ollama and a deeper llama.cpp build plus OpenAI-style server; Bash/API integrations for logs, commits, and docs; GPU offload tips, troubleshooting, and a CTA to automate your shell workflow.

Hands-on guide to using local AI to automate Bash privately and offline: explains benefits; installs Ollama or llama.cpp; pulls qwen2.5-coder:7b and llama3.1:8b; adds an ai() CLI helper; and shows four safe patterns: generate->lint->confirm scripts, explain one-liners, summarize logs, and emit jq/awk filters; plus model/hardware tips, a security checklist, troubleshooting, and concrete next steps.